Analysis module 3: Geographic differences in 16S microbiota composition (BM samples with strict filtering) RoVI study — 21 July, 2022

Sidebar

Sample summary

Taxon abundance profile

Alpha diversity

Longitudinal abundance plots

Beta diversity

Differential taxa

Longitudinal abundance comparisons

Session info

Sample summary

Input data: breastmilk samples (BM1, BM2, BM3)

  • n samples = 1124
  • n taxa: genera = 350; variants = 1712
  • n taxa (rarefied): genera = 349; variants = 1711
  • mean read count = 9.5075^{4}, s.d. = 1.13894^{5}

Statistical methods for mixed-effects models

  • Linear mixed-effects models fit with family ID as a random effect
  • Effect of country determined by comparing: (i) model with country/age vs (ii) model with age alone
  • Pairwise comparisons between countries (M vs I, U vs I, U vs M) corrected by FDR to control family error rate
  • OPV/IPV arms from India combined in primary comparison
  • Diversity in maternal samples compared by country via ANOVA with post-hoc Tukey test

Taxon abundance profile

Column

Genus-level

India

N: 753

Malawi

N: 243

UK

N: 128

RSV-level

India

Malawi

UK

Alpha diversity

Column

Genus-level

India vs Malawi vs UK - Shannon

India vs Malawi vs UK - richness

India vs Malawi vs UK with cross-sectional p values - Shannon

India vs Malawi vs UK with cross-sectional p values - richness

N by timepoint

     
      IND MLW  UK
  BM1 274  90  50
  BM2 247  79  39
  BM3 232  74  39

p values, longitudinal mixed-effects models (FDR-adjusted, 4 decimal places)

        Shannon Richness
MLW-IND   1e-04   0.0121
UK-IND    0e+00   0.0000
UK-MLW    0e+00   0.0000

p values, India (OPV) vs India (IPV)

                        Shannon Richness
India (OPV)-India (IPV)  0.0205   0.0368

p values, cross-sectional models

week of life 1
        Shannon Richness
MLW-IND  0.4736   0.0163
UK-IND   0.0000   0.0000
UK-MLW   0.0000   0.0000
week of life 7
        Shannon Richness
MLW-IND  0.0058   0.2738
UK-IND   0.0000   0.0000
UK-MLW   0.0004   0.0000
week of life 11
        Shannon Richness
MLW-IND  0.0029   0.9821
UK-IND   0.0000   0.0000
UK-MLW   0.0117   0.0000

Longitudinal abundance plots

Infants - India vs UK vs Malawi

N by timepoint

     
      India Malawi  UK
  BM1   274     90  50
  BM2   247     79  39
  BM3   232     74  39

Infants - India (OPV) vs India (IPV)

Beta diversity

Column

PERMANOVA - unweighted

Inter-individual differences among all infant samples
Permutation: free
Number of permutations: 999

Terms added sequentially (first to last)

            Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
family_ID  460     114.4 0.24869  1.3751 0.48825  0.001 ***
Residuals  663     119.9 0.18085         0.51175           
Total     1123     234.3                 1.00000           
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Differences by country stratified by age
             week         R2     p   n
1  week of life 1 0.08727212 0.001 414
2  week of life 7 0.07211099 0.001 365
3 week of life 11 0.06956617 0.001 345

Summary plot

PERMANOVA - weighted

Inter-individual differences among all infant samples
Permutation: free
Number of permutations: 999

Terms added sequentially (first to last)

            Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
family_ID  460    116.15 0.25250  1.3084 0.47584  0.001 ***
Residuals  663    127.94 0.19298         0.52416           
Total     1123    244.10                 1.00000           
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Differences by country stratified by age
             week         R2     p   n
1  week of life 1 0.03514650 0.001 414
2  week of life 7 0.04569780 0.001 365
3 week of life 11 0.04065342 0.001 345

Summary plot

Differential taxa

Column

Genus-level

Cross-validation accuracy, India vs Malawi vs UK

Pre-vaccination comparisons

Differential abundance plots - significant by Fisher’s test or Aldex2 (FDR p <0.05)

Longitudinal abundance comparisons

Column

Genus-level

Sample profile - country


 India Malawi     UK 
   753    243    128 

Sample profile - number of infants


 India Malawi     UK 
   302    108     51 

India vs UK

Number of genera tested: 58
Number of discriminant genera (FDR p < 0.05): 32 (India = 28; UK = 4) Failed to converge: 0

India vs Malawi

Number of genera tested: 70
Number of discriminant genera (FDR p < 0.05): 44 (India = 29; Malawi = 15) Failed to converge: 0

Malawi vs UK

Number of genera tested: 54
Number of discriminant genera (FDR p < 0.05): 32 (Malawi = 26; UK = 6) Failed to converge: 1

Construct tree

Longitudinal abundance plots for major genera

Session info

R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] FSA_0.9.1                   ALDEx2_1.26.0              
 [3] zCompositions_1.4.0         truncnorm_1.0-8            
 [5] NADA_1.6-1.1                survival_3.2-3             
 [7] MASS_7.3-55                 sjstats_0.18.0             
 [9] ggExtra_0.9                 formattable_0.2.1          
[11] NBZIMM_1.0                  inlmisc_0.5.2              
[13] decontam_1.14.0             ggtree_3.2.1               
[15] wesanderson_0.3.6           phangorn_2.8.1             
[17] ape_5.6-1                   DECIPHER_2.22.0            
[19] RSQLite_2.2.9               Biostrings_2.62.0          
[21] XVector_0.34.0              cowplot_1.1.1              
[23] scales_1.1.1                RVAideMemoire_0.9-81       
[25] DescTools_0.99.44           ggsignif_0.6.3             
[27] binom_1.1-1                 shiny_1.7.1                
[29] randomcoloR_1.1.0.1         DESeq2_1.34.0              
[31] SummarizedExperiment_1.24.0 Biobase_2.54.0             
[33] MatrixGenerics_1.6.0        GenomicRanges_1.46.1       
[35] GenomeInfoDb_1.30.0         IRanges_2.28.0             
[37] S4Vectors_0.32.3            BiocGenerics_0.40.0        
[39] crossval_1.0.4              UpSetR_1.4.0               
[41] labdsv_2.0-1                mgcv_1.8-38                
[43] nlme_3.1-155                ggpubr_0.4.0               
[45] data.table_1.14.2           corrplot_0.92              
[47] ZIBR_0.1                    vegan_2.5-7                
[49] lattice_0.20-45             permute_0.9-7              
[51] randomForest_4.6-14         matrixStats_0.61.0         
[53] lme4_1.1-27.1               Matrix_1.4-0               
[55] reshape2_1.4.4              pheatmap_1.0.12            
[57] DT_0.20                     plotly_4.10.0              
[59] cluster_2.1.2               tidyr_1.1.4                
[61] dplyr_1.0.4                 magrittr_2.0.1             
[63] plyr_1.8.6                  kableExtra_1.3.4           
[65] gridExtra_2.3               RColorBrewer_1.1-2         
[67] knitr_1.37                  ggplot2_3.3.5              
[69] phyloseq_1.38.0            

loaded via a namespace (and not attached):
  [1] estimability_1.3       coda_0.19-4            bit64_4.0.5           
  [4] multcomp_1.4-18        DelayedArray_0.20.0    KEGGREST_1.34.0       
  [7] RCurl_1.98-1.5         generics_0.1.1         TH.data_1.1-0         
 [10] terra_1.5-12           proxy_0.4-26           bit_4.0.4             
 [13] webshot_0.5.2          xml2_1.3.3             httpuv_1.6.5          
 [16] assertthat_0.2.1       xfun_0.29              jquerylib_0.1.4       
 [19] evaluate_0.14          promises_1.2.0.1       fansi_1.0.2           
 [22] igraph_1.2.11          DBI_1.1.2              geneplotter_1.72.0    
 [25] htmlwidgets_1.5.4      purrr_0.3.4            ellipsis_0.3.2        
 [28] backports_1.4.1        V8_4.0.0               insight_0.15.0        
 [31] annotate_1.72.0        vctrs_0.3.8            sjlabelled_1.1.8      
 [34] abind_1.4-5            cachem_1.0.6           withr_2.4.3           
 [37] checkmate_2.0.0        rgdal_1.5-28           emmeans_1.7.2         
 [40] treeio_1.18.1          svglite_2.0.0          lazyeval_0.2.2        
 [43] crayon_1.4.2           flexdashboard_0.5.2    genefilter_1.76.0     
 [46] labeling_0.4.2         pkgconfig_2.0.3        rlang_1.0.1           
 [49] lifecycle_1.0.1        miniUI_0.1.1.1         sandwich_3.0-1        
 [52] modelr_0.1.8           datawizard_0.2.2       aplot_0.1.2           
 [55] raster_3.5-11          carData_3.0-5          zoo_1.8-9             
 [58] Rhdf5lib_1.16.0        boot_1.3-28            png_0.1-7             
 [61] viridisLite_0.4.0      parameters_0.16.0      rootSolve_1.8.2.3     
 [64] bitops_1.0-7           rhdf5filters_1.6.0     blob_1.2.2            
 [67] stringr_1.4.0          rstatix_0.7.0          gridGraphics_0.5-1    
 [70] memoise_2.0.1          zlibbioc_1.40.0        compiler_4.1.2        
 [73] cli_3.2.0              ade4_1.7-18            patchwork_1.1.1       
 [76] tidyselect_1.1.1       stringi_1.7.6          highr_0.9             
 [79] yaml_2.2.1             locfit_1.5-9.4         grid_4.1.2            
 [82] sass_0.4.0             fastmatch_1.1-3        tools_4.1.2           
 [85] lmom_2.8               rstudioapi_0.13        foreach_1.5.1         
 [88] gld_2.6.4              farver_2.1.0           Rtsne_0.15            
 [91] RcppZiggurat_0.1.6     digest_0.6.29          quadprog_1.5-8        
 [94] Rcpp_1.0.8             car_3.0-12             broom_0.7.5           
 [97] performance_0.8.0      later_1.3.0            httr_1.4.2            
[100] AnnotationDbi_1.56.2   effectsize_0.6.0       colorspace_2.0-2      
[103] rvest_1.0.2            XML_3.99-0.8           splines_4.1.2         
[106] yulab.utils_0.0.4      tidytree_0.3.7         expm_0.999-6          
[109] sp_1.4-6               multtest_2.50.0        Exact_3.1             
[112] ggplotify_0.1.0        systemfonts_1.0.2      xtable_1.8-4          
[115] jsonlite_1.7.3         nloptr_1.2.2.3         Rfast_2.0.4           
[118] ggfun_0.0.5            R6_2.5.1               pillar_1.6.4          
[121] htmltools_0.5.2        mime_0.12              glue_1.6.0            
[124] fastmap_1.1.0          minqa_1.2.4            BiocParallel_1.28.3   
[127] class_7.3-20           codetools_0.2-18       mvtnorm_1.1-3         
[130] utf8_1.2.2             bslib_0.3.1            tibble_3.1.6          
[133] curl_4.3.2             rmarkdown_2.11         biomformat_1.22.0     
[136] munsell_0.5.0          e1071_1.7-9            rhdf5_2.38.0          
[139] GenomeInfoDbData_1.2.7 iterators_1.0.13       sjmisc_2.8.9          
[142] gtable_0.3.0           bayestestR_0.11.5     

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